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1 Autoatic Generation of Related Work Sections in Scientific Papers: An Optiization Approach Yue Hu and Xiaojun Wan Institute of Coputer Science and Technology The MOE Key Laboratory of Coputational Linguistics Peking University, Beijing, China Abstract In this paper, we investigate a challenging task of autoatic related work generation. Given ultiple reference papers as input, the task ais to generate a related work section for a target paper. The generated related work section can be used as a draft for the author to coplete his or her final related work section. We propose our Autoatic Related Work Generation syste called ARWG to address this task. It first exploits a PLSA odel to split the sentence set of the given papers into different topic-biased parts, and then applies regression odels to learn the iportance of the sentences. At last it eploys an optiization fraework to generate the related work section. Our evaluation results on a test set of 15 target papers along with their reference papers show that our proposed ARWG syste can generate related work sections with better quality. A user study is also perfored to show ARWG can achieve an iproveent over generic ulti-docuent suarization baselines. 1 Introduction The related work section is an iportant part of a paper. An author often needs to help readers to understand the context of his or her research proble and copare his or her current work with previous works. A related work section is often used for this purpose to show the differences and advantages of his or her work, copared with related research works. In this study, we attept to autoatically generate a related work section for a target acadeic paper with its reference papers. This kind of related work sections can be used as a basis to reduce the author s tie and effort when he or she wants to coplete his or her final related work section. Autoatic related work section generation is a very challenging task. It can be considered a topic-biased, ultiple-docuent suarization proble. The input is a target acadeic paper, which has no related work section, along with its reference papers. The goal is to create a related work section that describes the related works and addresses the relationship between the target paper and the reference papers. Here we assue that the set of reference papers has been given as part of the input. Existing works in the NLP and recoendation systes counities have already focused on the task of finding reference papers. For exaple, citation prediction (Nallapati et al., 28) ais at finding individual paper citation patterns. Generally speaking, autoatic related work section generation is a strikingly different proble and it is uch ore difficult in coparison with general ulti-docuent suarization tasks. For exaple, ulti-docuent suarization of news articles ais at synthesizing contents of siilar news and reoving the redundant inforation contained by the different news articles. However, each scientific paper has uch specific content to state its own work and contribution. Even for the papers that investigate the sae research topic, their contributions and contents can be totally different. The related work section generation task needs to find the specific contributions of individual papers and arrange the into one or several paragraphs. In this study, we focus on the proble of autoatic related work section generation and propose a novel syste called ARWG to address the 1624 Proceedings of the 214 Conference on Epirical Methods in Natural Language Processing (EMNLP), pages , October 25-29, 214, Doha, Qatar. c 214 Association for Coputational Linguistics

2 proble. For the target paper, we assue that the abstract and introduction sections have already been written by the author and they can be used to help generate the related work section. For the reference papers, we only consider and extract the abstract, introduction, related work and conclusion sections, because other sections like the ethod and evaluation sections always describe the extree details of the specific work and they are not suitable for this task. Then we generate the related work section using both sentence sets which are extracted fro the target paper and reference papers, respectively. Firstly, we use a PLSA odel to group both sentence sets of the target paper and its reference papers into different topic-biased clusters. Secondly, the iportance of each sentence in the target paper and the reference papers is learned by using two different Support Vector Regression (SVR) odels. At last, a global optiization fraework is proposed to generate the related work section by selecting sentences fro both the target paper and the reference papers. Meanwhile, the fraework selects sentences fro different topic-biased clusters globally. Experiental results on a test set of 15 target papers show our ethod can generate related work sections with better quality than those of several baseline ethods. With the ROUGE toolkit, the results indicate the related work sections generated by our syste can get higher ROUGE scores. Moreover, our related work sections can get higher rating scores based on a user study. Therefore, our related work sections can be uch ore suitable for the authors to prepare their final related work sections. 2 Related Work There are few studies to directly address autoatic related work generation. Hoang and Kan (21) proposed a related work suarization syste given the set of keywords arranged in a hierarchical fashion that describes the paper s topic. They used two different rule-based strategies to extract sentences for general topics as well as detailed ones. A few studies focus on ulti-docuent scientific article suarization. Agarwal et al., (211) introduced an unsupervised approach to the proble of ulti-docuent suarization. The input is a list of papers cited together within the sae source article. The key point of this approach is a topic based clustering of fragents extracted fro each co-cited article. They rank all the clusters using a query generated fro the context surrounding the co-cited list of papers. Yeloglu et al., (211) copared four different approaches for ulti-docuent scientific articles suarization: MEAD, MEAD with corpus specific vocabulary, LexRank and W3SS. Other studies investigate ainly on the singledocuent scientific article suarization. Early works including (Luhn 1958; Baxendale 1958; Eduundson 1969) tried to use various features specific to scientific text (e.g., sentence position, or rhetorical clues features). They have proved that these features are effective for the scientific article suarization. Citation inforation has been already shown effective in suarize the scientific articles. Works including (Mei and Zhai 28; Qazvinian and Radev 28; Schwartz and Hearst 26; Mohaad et al., 29) eployed citation inforation for the single scientific article suarization. Earlier work (Nakov et al., 24) indicated that citation sentences ay contain iportant concepts that can give useful descriptions of a paper. Various ethods have been proposed for news docuent suarization, including rule-based ethods (Barzilay and Elhadad 1997; Marcu and Daniel 1997), graph-based ethods (Mani and Bloedorn 2; Erkan and Radev 24; Michalcea and Tarau 25), learning-based ethods (Conroy et al., 21; Shen et al., 27; Ouyang et al., 27; Galanis et al., 28), optiizationbased ethods (McDonald 27; Gillick et al., 29; Xie et al., 29; Berg-Kirkpatrick et al., 211; Lei Huang et al., 211; Woodsend et al., 212; Galanis 212), etc. The ost relevant work is (Hoang and Kan, 21) as entioned above. They also assued the set of reference papers was given as part of the input. They also adopt the hierarchical topic tree that describes the topic structure in the target paper as an essential input for their syste. However, it is non-trivial to build the hierarchical topic tree. Moreover, they do not consider the content of the target paper to construct the related work section, which is actually crucial in the related work section. To the best of our knowledge, no previous works have used supervised learning and optiization fraework to deal with the ultiple scientific article suarization tasks. 3 Proble Analysis and Corpus 3.1 Proble Analysis 1625

3 There has been a substantial aount of research on autoatic taxonoy induction. As we entioned earlier, two ain approaches are pattern-based and clustering-based. Pattern-based approaches are the ain trend for autoatic taxonoy induction. Pattern-based approaches started fro and still pay a great deal of attention to the ost coon is-a relations. Clustering-based approaches usually represent word contexts as vectors and cluster words based on siilarities of the vectors (Brown et al., 1992; Lin, 1998). Many clustering-based approaches face the challenge of appropriately labeling non-leaf clusters. In this paper, we take an increental clustering approach,... The advantage of the increental approach is that it eliinates the trouble of inventing cluster labels and concentrates on placing ters in the correct positions in a taxonoy hierarchy. The work by Snow et al. (26) is the ost siilar to ours Moreover, our approach eploys heterogeneous features fro a wide range; while their approach only used syntactic dependency. We firstly analyze the structure of related work sections briefly. By using exaples for illustration, we can gain insight on how to generate related work sections. A specific related work exaple is shown in Figure 1. This related work section introduces previous related works for a paper on Autoatic Taxonoy Induction. Fro Figure 1, we can have a glance at the structure of related work sections. Related work sections usually discuss several different topics, such as pattern-based and cluster-based approaches shown in the Figure 1. Besides the knowledge of previous works, the author often copares his own work with the previous works. The differences and advantages are generally entioned. The exaple in Figure 1 also indicates this phenoenon. Therefore, we design our syste to generate related work sections according to the related work section structure entioned above. Our syste takes the target paper for which a related work section needs to be drafted besides its reference papers as input. The goal of our syste is to generate a related work section with the above structure. The generated related work section should have several topic-biased parts. The author's own work is also needed to be described and its difference with other works is needed to be ephasized on. 3.2 Corpus and Preprocessing We build a corpus that contains acadeic papers and their corresponding reference papers. The acadeic papers are selected fro the ACL Anthology 1. The ACL Anthology currently hosts 1 Two different topics Coparison with the author s work Figure 1: A saple related work section (Yang and Callan 29) over 24,5 papers fro ajor conferences such as ACL, EMNLP, COLING in the fields of coputational linguistics and natural language processing. We reove the papers that contain related work sections with very short length, and randoly select 15 target papers to construct our whole corpus. The papers are all in PDF forat. We extract their texts by using PDFlib 2 and detect their physical structures of paragraphs, subsections and sections by using ParsCit 3. For the target papers, the related work sections are directly extracted as the gold suaries. The references are also extracted. For the references that can be found in the ACL Anthology, we download the fro the ACL Anthology. The other reference papers are searched and downloaded by using Google Scholar. References to books and PhD theses are discarded, for their verbosity ay change the proble drastically (Mihalcea and Ceylan, 27). The input of our syste includes the abstract and introduction sections of the target paper, and the abstract, introduction, related work and conclusion sections of the reference papers. As entioned above, the ethod and evaluation sections in the reference papers are not used as input because these sections usually describe extree details of the ethods and evaluation results and they are not suitable for related work generation. Note that it is reasonable to ake use of the abstract and introduction sections of the target paper to help generate the related work section, because an author usually has already written the abstract and introduction sections before he or she wants to write the related work section for the target paper. Otherwise, we cannot get any inforation about the author s own work. All other sections in the target paper are not used. 4 Our Proposed Syste 4.1 Overview In this paper, we propose a syste called ARWG to autoatically generate a related work section for a given target paper. The architecture of our syste is shown in Figure 2. We take both the target paper and its reference papers as input and they are represented by several sections entioned in Section 3.2. After preprocessing, we extract the feature vectors for sentences in the target paper and the reference papers, respective

4 ly. The iportance scores for sentences in the target paper and the reference papers are assigned by using two SVR based sentence scoring odels. The two SVR odels are trained for sentences in the target paper and the reference papers, respectively. Meanwhile, a topic odel is applied to the whole set of sentences in both the target paper and reference papers. The sentences are grouped into several different topic-biased clusters. The sentences with iportance scores and topic cluster inforation are taken as the input for the global optiization fraework. The optiization fraework extracts sentences to describe both the author s own work and background knowledge. More details of each part will be discussed in the following sections. 4.2 Topic Model Learning Target paper Sentence Score Assessent(target) Preprocessing Sentence Score Assessent(reference) Optiization Fraework Postprocessing Reference papers Figure 2: Syste Architecture Topic Model Related Work Section As entioned in the previous section, the related work section usually addresses several different topics. The topics ay be different research thees or different aspects of a broad research thee. The related work section should describe the specific details for each topic, respectively. Therefore, we ai to discover the hidden topics of the input papers, and we use the Probabilistic latent seantic analysis (PLSA) (Hofann, 1999) to solve this proble. The PLSA approach odels each word in a docuent as a saple fro a ixture odel. The ixture coponents are ultinoial rando variables that can be viewed as representations of topics. Different words in a docuent ay be generated fro different topics. Each docuent is represented a list of ixing proportions for these ixture coponents and can be reduced to a probability distribution on a fixed set of topics. Considering that the sentences in one paper ay relate to different topics, we treat each sentence as a docuent d. We treat the noun phases in the sentences as the words w. In order to extract the noun phrases, chunking ipleented by the OpenNLP toolkit 4 is applied to the sentences. Noun phrases that contain words such as paper and data are discarded. Then the sentences with their corresponding noun phrases are taken as input into the PLSA odel. Here both the sentences in the target paper and the sentences in the reference papers are treated the sae in the odel. Finally, we can get the sentence set with topic inforation and use it in the subsequent steps. Each sentence has a topic weight t in each topic. 4.3 Sentence Iportant Assessent In our proposed syste, sentence iportance assessent ais to assign an iportance score to each sentence in the target paper and reference papers. The score of each sentence will be used in the subsequent optiization fraework. We propose to use the support vector regression odel to achieve this goal. In the above topic odel learning process, we do not distinguish the sentences in the target paper and reference papers. In contrast, we train two different support vector regression odels separately for the sentences in the target paper and the sentences in the reference papers. In the related work section, the sentences that describe the author s own work usually address the differences fro the related works, while the sentences that describe the related works often focus on the specific details. We think the two kinds of sentences should be treated differently. Scoring Method To construct training data based on the papers collected, we apply a siilarity scoring ethod to assign the iportance scores to the sentences in the papers. The ain hypothesis is that the sentences in the gold related work sections should suarize the target paper and reference papers as well. Thus the sentences in the papers which are ore siilar to the sentences in the gold related work sections should be considered ore iportant and suitable to be selected. Our scoring ethod should assign higher scores to the

5 We define the iportance score of a sentence in the papers as below: score(s) = ax s i S (si(s, s i )) (1) where s is a sentence in the papers, S is the set of the sentences in the corresponding gold related work section. The standard cosine easure is eployed as the siilarity function. Considering the difference between the sentences that describe the author s work and the sentences that describe the related works, we split the set of sentences in the gold related work section into two parts: one discusses the author s own work and the other introduces the related works. We observe that sentences related to the author s own work often feature specific words or phrases (such as we, our work, in this paper etc.) in the related work section. So we check the sentences about whether they contain clue words or phrases (i.e., in this paper, our work and 18 other phrases). If the clue phrase check fails, the sentence belongs to the related work part. If not, it belongs the own work part. Thus for the sentences in the target paper, S is the set of sentences in the own work part of the gold related work section, while for the sentences in the reference papers, S is the set of sentences in the related work part of the gold related work section. Then we can use the scoring ethod to copute the target scores of the sentences in the training set. It is noteworthy that two SVR odels can be trained on the two parts of the training data, respectively. Feature Each sentence is represented by a set of features. The coon features used for the sentences of the target paper and reference papers are shown in Table 1. The additional features applied to the sentences of the target paper are introduced in Table 2. Here, s is a sentence that needs to extract features. th is paper title, section headings and subsection headings set of the reference papers or target paper for the two SVR odels, respectively. Each feature with * represent a feature set that contains siilar features. All the features are scaled into [, 1]. Thus we can learn SVR odels based on the features and iportance scores of the sentences, and then use the odels to predict an iportance score for each sentence in the test set. The SVR odels are trained and applied for the target paper and reference papers, respectively. Table 1: Coon features eployed in the SVR odels Feature Si(s, th) WS(s,th) SP(s) Description The siilarity between s and each title in th; Stop words are reoved and steing is eployed. Nuber of words shared by s and th. The position of s in its section or subsection PTI(s) The parse tree inforation of s, including the nuber of noun phrase and verb phrases, the depth of the parse tree, etc. IsHead(s) IsEnd(s) SWP(s) Length(s) Length_rw(s) SI(s) CluePhrase(s) Indicates whether s is the first sentence of the section or subsection Indicates whether s is the last sentence of the section or subsection The percentage of the stop words The length of sentence s The length of s after reoving stop words The section index of s that indicates which section s is fro. Indicates whether a clue phrase appears in s. the clue phrases include our work, propose and other 2 words. Each clue phrase corresponds to one feature. Table 2: Additional features for sentences in the target paper Feature Description HasCitation(s) PhraseForCp(s) Indicates whether s contains a citation Indicates whether s contains words or phrases used for coparison such as in contrast, instead and other 26 words. Each word or phrase corresponds to one feature. 4.4 A Global Optiization Fraework In the above steps, we can get the predicted iportance score and topic inforation for each sentence in the target paper and reference papers. Here, we introduce a global optiization fraework to generate the related work section. According to the structure of the related work section entioned above, the related work section usually discusses several topics. In each topic, the related works and their details are introduced. Besides, the author often copares his own work with these previous works. Therefore, we propose to forulate the generation as an optiization proble. Basically, we will be searching for a set of sentences to optiize the objective function. 1628

6 Table 3: Notations used in this section Sybol Description sr i /st i lr i /lt i wr i /wt i xr ij /xt ij nr/nt t ij B y i c bi L ax L j B B i Sr /St λ 1, λ 2, λ 3 the sentence in the reference/target paper the length of sentence sr i / st i the iportance score of sr i /st i indicates whether sr i /st i is selected into the part of topic j in the generated related work section the nuber of sentences in the reference/target papers the topic count the topic weight of sr i /st i in topic j fro the PLSA odel the set of unique bigras indicates whether bigra b i is included in the result the count of the occurrences of bigra b i in the both target paper and reference papers the axiu word count of the related work section the axiu word count of the part of topic j which depends on the percentage of sentences belong to topic j the total set of bigras in the whole paper set the set of bigras that sentence sr i /st i contains the set of sentences that include bigra b in the reference/target papers paraeters for tuning To design the objective function, three aspects should be considered: 1) First, the related work section we generate should introduce the previous works well. In our assuption, sentences with higher iportance scores are better to be selected. In addition, very short sentences should be penalized. So we introduce the first part of our objective function below: nr i=1 (lr i wr i j=1 t ij xr ij ) (2) We add the sentence length as a ultiplication factor in order to penalize the very short sentences, or the objective function tends to select ore and shorter sentences. At the sae tie, the objective function does not tend to select the very long sentences. The total length of the sentences selected is fixed. So if the objective function tends to select the longer sentences, the fewer sentences can be selected. A tradeoff needs to be ade between the nuber and the average length of the sentences selected. The constraints introduced below ensure that the sentence can only be selected into one topic and the topic weight is used to easure the degree that the sentence is relevant to the specific topic. 2) Second, siilar to the first part, we should consider the own work part of the related work section. Thus the second part of our objective function is shown as follows: nt i=1 (lt i wt i j=1 t ij xt ij ) (3) 3) At last, redundancy reduction should be considered in the objective function. The last part of the objective function is shown below: B i=1 c bi y i (4) The intuition is that the ore unique bigras the related work section contains, the less redundancy the related work section has. We add c bi as the weight of the bigra in order to include ore iportant bigras. By cobing all the parts defined above, we have the following full objective function: ax λ nr 1 ( lr i i=1 wr xr,xt αl i j=1 t ij xr ij ) + ax nt lt λ 2 ( i i=1 wt (1 α)l i j=1 t ij xt ij ) + ax B λ 3 c b i y i i=1 (5) B Subject to: nr nt i=1 lr i xr ij + i=1 lt i xt ij < L j, for j = 1,, (6) nr i=1 j=1 lr i xr ij < αl ax (7) nt i=1 j=1 lt i xt ij < (1 α)l ax (8) j=1 xr ij 1, for i = 1,, nr (9) j=1 xt ij 1, for i = 1,, nt (1) bk B i y k B i j=1 xr ij, for i = 1,, nr (11) bk B i y k B i j=1 xt ij, for i = 1,, nt (12) sr i Sr k j=1 xr ij + st i St k j=1 xt ij y k, k = 1, B (13) xr ij, xt ij, y i {,1} (14) All the three parts in the objective function are noralized to [, 1] by using the axiu length L ax and the total nuber of bigras B. λ 1, λ 2 and λ 3 are paraeters for tuning the three parts and we set λ 1 +λ 2 +λ 3 = 1. We explain the constraints as follows: Constraint (6): It ensures that the total word count of the part of topic j does not exceed L j. Constraints (7), (8): The two constraints try to balance the lengths of the previous works part and the own work part, respectively. α is set to 2/3. Constraints (9), (1): These two constraints guarantee that the sentence can only be included into one topic. 1629

7 Constraints (11), (12): When these two constraints hold, all bigras that s i has are selected if s i is selected. Constraint (13): This constraint akes sure that at least one sentence in Sr or St is selected if bigra b is selected. Therefore, we transfor our optiization proble into a linear prograing proble. We solve this linear prograing proble by using the IBM CPLEX optiizer 5. It generally takes tens of seconds to solve the proble and it is very efficient. Finally, ARWG post-processes sentences to iprove readability, including replacing agentive fors with a citation to the specific article (e.g., our work (Hoang and Kan, 21) ) for the sentences extracted fro reference papers. The sentences belonging to different topics are placed separately. 5 Evaluation 5.1 Evaluation Setup To set up our experients, we divide our dataset which contains 15 target papers and their reference papers into two parts: 7 target papers for training, 15 papers for test and the other 2 papers for validation. The PLSA topic odel is applied to the whole dataset. We train two SVR regression odels based on the own work part and the previous work part of the training data and apply the odels to the test data. The global optiization fraework is used to generate the related work sections. We set the axiu word count of the generated related work section to be equal to that of the gold related work section. The paraeter values of λ 1, λ 2 and λ 3 are set to.3,.1 and.6, respectively. The paraeter values are tuned on the validation data. We copare our syste with five baseline systes: MEAD-WT, LexRank-WT, ARWG-WT, MEAD and LexRank. MEAD 6 (Radev et al., 24) is an open-source extractive ultidocuent suarizer. LexRank 7 (Eran and Radev, 24) is a ulti-docuent suarization syste which is based on a rando walk on the siilarity graph of sentences. We also ipleent the MEAD, LexRank baselines and our ethod 5 www-1.ib.co/software/integration/optiization/cplexoptiizer/ 6 7 In our experients, LexRank perfors uch better than the ore coplex variant - C-LexRank (Qazvinian and Radev, 28), and thus we choose LexRank, rather than C- LexRank, to represent graph-based suarization ethods for coparison in this paper. with only the reference papers (i.e. the target paper s content is not considered). Those ethods are signed by -WT. To evaluate the effectiveness of the SVR odels we eploy, we ipleent a baseline syste RWGOF that uses the rando walk scores as the iportant scores of the sentences and take the scores as inputs for the sae global optiization fraework as our syste to generate the related work section. The rando walk scores are coputed for the sentences in the reference papers and the target paper, respectively. We use the ROUGE toolkit to evaluate the content quality of the generated related work sections. ROUGE (Lin, 24) is a widely used autoatic suarization evaluation ethod based on n-gra coparison. Here, we use the F- Measure scores of ROUGE-1, ROUGE-2 and ROUGE-SU4. The odel texts are set as the gold related work sections extracted fro the target papers, and word steing is utilized. ROUGE-N is an n-gra based easure between a candidate text and a reference text. The recall oriented score, the precision oriented score and the F-easure score for ROUGE-N are coputed as follows: ROUGE N Recall = S {Reference Text} gra n Count atch (gra n ) / S {Reference Text} gra n Count(gra n ) (15) ROUGE N Precision = S {Reference Text} gra n Count atch (gra n ) / S {Candidate Text} gra n Count(gra n ) (16) ROUGE N F easure = 2 ROUGE N Recall ROUGE N Precision / ROUGE N Recall + ROUGE N Precision (17) where n stands for the length of the n-gra gra n, and Count atch (gra n ) is the axiu nuber of n-gras co-occurring in a candidate text and a reference text. In addition, we conducted a user study to subjectively evaluate the related work sections to get ore evidences. We selected the related work sections generated by different ethods for 15 rando target papers in the test set. We asked three huan judges to follow an evaluation guideline we design and evaluate these related work sections. The huan judges are graduate students in the coputer science field and they did not know the identities of the evaluated related work sections. They were asked to give a rating on a scale of 1 (very poor) to 5 (very good) for the correctness, readability and usefulness of the related work sections, respectively: 163

8 λ1 ROUGE-1 ROUGE-2 ROUGE-SU4 λ1 ROUGE-1 ROUGE-2 ROUGE-SU λ2.6 λ λ λ Figure 3: Paraeter influences (horizontal, vertical axis are λ 1, λ 2, respectively, λ 3 = 1 λ 1 λ 2 ) 1) Correctness: Is the related work section actually related to the target paper? 2) Readability: Is the related work section easy for the readers to read and grasp the key content? 3) Usefulness: Is the related work section useful for the author to prepare their final related work section? Paired T-Tests are applied to both the ROUGE scores and rating scores for coparing ARWG and baselines and coparing the systes with WT and without WT. 5.2 Results and Discussion Table 4: ROUGE F-easure coparison results Method ROUGE-1 ROUGE-2 ROUGE- Mead- WT LexRank- WT ARWG- WT SU {1,2}.9987 {1,2} {1}#{2} Mead.4112 {1}.9642 {1} {1} LexRank {2}.19 {2}.1767 {2} ARWG {1 5} {1 5} {1 5} ( * represents pairwise t-test value p <.1; # represents p <.5; the nubers in the brackets represent the indices of the ethods copared, e.g. 1 for MEAD-WT, 2 for LexRank-WT, etc.) Table 5: Average rating scores of judges Method Correctness Readability Usefulness Mead LexRank ARWG # 3.42 # # ( *# represents pairwise t-test value p <.1, copared with Mead and LexRank, respectively.) Table 6: ROUGE F-easure coparison of different sentence iportance scores Method ROUGE-1 ROUGE-2 ROUGE-SU4 RWGOF ARWG The evaluation results over ROUGE etrics are presented in Table 4. It shows that our proposed syste can get higher ROUGE scores, i.e., better content quality. In our syste, we split the sentence set into different topic-biased parts, and the iportance scores of sentences in the target paper and reference papers are learned differently. So the obtained iportance scores of the sentences are ore reliable. The global optiization fraework considers the extraction of both the previous work part and the own work part. We can see the iportance of the own work part by coparing the results of the ethods with or without considering the own work part. MEAD, LexRank and our ethod all get a significant iproveent after considering the own work part by extracting sentences fro the target paper. The results also prove our assuption about the related work section structure. Figure 3 presents the fluctuation of ROUGE scores when tuning the paraeters λ 1, λ 2 and λ 3. We can see our ethod generally perfors better than the baselines. All the three parts in the objective function are useful to generate related work sections with good quality. The average scores rated by huan judges for each ethod are showed in Table 5. We can see that the related work sections generated by our syste are ore related to the target papers. Moreover, because of the good structure of our generated related work sections, our generated related work sections are considered ore readable and ore useful for the author to prepare the final related work sections. T-test results show that the perforance iproveents of our ethod over baselines are statistically significant on both autoatic and anual evaluations. Most of p-values for t-test are far saller than.1. Overall, the results indicate that our ethod can generate uch better related work sections 1631

9 than the baselines on both autoatic and huan evaluations. Table 6 shows the coparison results between ARWG and RWGOF. We can see ARWG perfors better than RWGOF. It proves that the SVR odels can better estiate the iportance scores of the sentences. For the SVR odels are trained fro the large dataset, the sentence scores predicted by the SVR odels can be ore reliable to be used in the global optiization fraework. 6 Conclusion and Future Work This paper proposes a novel syste called ARWG to generate related work sections for acadeic papers. It first exploits a PLSA odel to split the sentence set of the given papers into different topic-biased parts, and then applies regression odels to learn the iportance scores of the sentences. At last an optiization fraework is proposed to generate the related work section. Evaluation results show that our syste can generate uch better related work sections than the baseline ethods. In future work, we will ake use of citation sentences to iprove our syste. Citation sentences are the sentences that contains an explicit reference to another paper and they usually highlight the ost iportant aspects of the cited papers. So citation sentences are likely to contain iportant and rich inforation for generating related work sections. Acknowledgents The work was supported by National Natural Science Foundation of China ( , ), Beijing Nova Progra (28B3) and National Hi-Tech Research and Developent Progra (863 Progra) of China (212AA1111). We also thank the anonyous reviewers for very helpful coents. The corresponding author of this paper, according to the eaning given to this role by Peking University, is Xiaojun Wan. Reference Nitin Agarwal, Kiran Gvr, Ravi Shankar Reddy, and Carolyn Penstein Rosé Towards ultidocuent suarization of scientific articles: aking interesting coparisons with SciSu. In Proceedings of the Workshop on Autoatic Suarization for Different Genres, Media, and Languages, pp Association for Coputational Linguistics. Phyllis B. Baxendale Machine-ade index for technical literature: an experient. IBM Journal of Research and Developent 2, no. 4: Taylor Berg-Kirkpatrick, Dan Gillick, and Dan Klein Jointly learning to extract and copress. In Proceedings of the 49th Annual Meeting of the Association for Coputational Linguistics: Huan Language Technologies-Volue 1, pp Association for Coputational Linguistics. Chih-Chung Chang, and Chih-Jen Lin LIBSVM: a library for support vector achines. ACM Transactions on Intelligent Systes and Technology (TIST) 2, no. 3: 27. John M. Conroy, and Dianne P. O'leary. 21. Text suarization via hidden arkov odels. In Proceedings of the 24th annual international ACM SIGIR conference on Research and developent in inforation retrieval, pp ACM. Harold P. Edundson New ethods in autoatic extracting. Journal of the ACM (JACM) 16, no. 2: Günes Erkan, and Dragoir R. Radev. 24. LexPageRank: Prestige in Multi-Docuent Text Suarization. In EMNLP, vol. 4, pp Günes Erkan, and Dragoir R. Radev. 24. LexRank: Graph-based lexical centrality as salience in text suarization. J. Artif. Intell. Res.(JAIR) 22, no. 1: Diitrios Galanis, Gerasios Lapouras, and Ion Androutsopoulos Extractive Multi- Docuent Suarization with Integer Linear Prograing and Support Vector Regression. In COLING, pp Diitrios Galanis, and Prodroos Malakasiotis. 28. Aueb at tac 28. InProceedings of the TAC 28 Workshop. Dan Gillick, and Benoit Favre. 29. A scalable global odel for suarization. InProceedings of the Workshop on Integer Linear Prograing for Natural Langauge Processing, pp Association for Coputational Linguistics. Cong Duy Vu Hoang, and Min-Yen Kan. 21. Towards autoated related work suarization. In Proceedings of the 23rd International Conference on Coputational Linguistics: Posters, pp Association for Coputational Linguistics. Lei Huang, Yanxiang He, Furu Wei, and Wenjie Li. 21. Modeling docuent suarization as ultiobjective optiization. In Intelligent Inforation Technology and Security Inforatics (IITSI), 21 Third International Syposiu on, pp IEEE. 1632

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